Prediction of delayed graft function after deceased donor kidney transplantation: development and temporal validation in a single-center cohort.

backgroundDelayed graft function (DGF) is a common complication of kidney transplantation and is associated with poor graft outcome. Accurate prediction of DGF is crucial for timely intervention and optimization of post-transplant management. Although predictive models for DGF have been reported, whether more complex supervised learning approaches provide meaningful incremental value over simpler and more interpretable models remains uncertain.

methodsIn this single-center study, we conducted a secondary analysis of an ongoing prospective cohort of consecutive kidney transplant recipients at our center, using data from patients enrolled between June 2023 and April 2025 as the development cohort. The models were validated in an independent retrospective cohort of consecutive recipients transplanted between January 2019 and May 2023. Nine individual supervised machine learning algorithms and 84 stacked ensemble models were constructed and compared. Predictors were selected in the development cohort using univariate analysis followed by bidirectional stepwise logistic regression (LR) based on the Akaike information criterion. Model performance in the validation cohort was assessed in terms of discrimination, calibration, and clinical utility. Model interpretation was supplemented using Shapley additive explanations.

resultsThe development cohort and validation cohort included 140 and 314 kidney transplant recipients, respectively. Four donor-derived variables were retained in the final predictor set: donor body mass index, donation after circulatory death status, cold ischemia time, and donor terminal serum creatinine. Among the nine individual models, the LR model showed the best overall performance in the validation cohort, with an area under the receiver operating characteristic curve of 0.839. The best stacked ensemble model showed similar performance and did not provide meaningful incremental benefit over the LR model.

conclusionsIn conclusion, in this single-center cohort with independent temporally distinct validation, a parsimonious LR model based on four routinely available donor variables performed comparably to more complex supervised learning algorithms and stacked ensemble approaches for DGF prediction. These findings support the practical value of a simple and interpretable model for pre-transplant DGF risk stratification, although further external validation is needed before broader clinical application.